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AI Engineer with less than a year in conversational AI and machine learning.
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Assessing your cultural and operational fit
Entry-level AI Engineer / Machine Learning Engineer with hands-on experience developing conversational AI systems and machine learning applications. Skilled in Python, prompt engineering, RAG, LangChain, FAISS, and transformer models. Developed and deployed a production-grade AI chatbot via FastAPI enabling real-time natural-language queries. Seeking to apply NLP and generative AI expertise to enhance customer support solutions.
Savitribai Phule Pune University
B.E. · Artificial Intelligence & Machine Learning
August 1, 2022 – June 30, 2026
NV Analytical Solutions
Machine Learning Intern
January 1, 2025 – June 1, 2025
India
Demand Forecasting MLOps Pipeline
June 24, 2026 – Present
Engineered a production-ready end-to-end MLOps pipeline for e-commerce demand forecasting, training and comparing 4 models (LinearRegression, RandomForest, XGBoost, Prophet) with time-based 80/20 splits, and registering the best model to MLflow Model Registry with automated metric logging (RMSE, MAE, R2, MAPE). Built a FastAPI REST service with /predict, /health, and /monitoring/report endpoints; containerized the full stack using Docker Compose (API + MLflow server); and implemented GitHub Actions CI/CD to automate testing, model retraining, and Docker Hub deployment on every push to main. Integrated Evidently AI for data drift monitoring with auto-retraining trigger when drift score exceeds 0.3; managed data versioning with DVC and feature engineering pipeline generating temporal, lag, and rolling-window features across 28-day historical windows.
RAG-Based YouTube Chatbot
June 24, 2026 – Present
Architected an end-to-end Retrieval-Augmented Generation (RAG) pipeline to enable intelligent Q&A over YouTube video transcripts. Engineered complete document ingestion workflow including text chunking, embedding generation, and semantic retrieval using LangChain. Integrated vector databases (FAISS/Chroma) for efficient similarity search and context retrieval. Designed prompts and fine-tuned a transformer-based LLM using Hugging Face Transformers to improve answer relevance and accuracy.
Cultural Fit Analysis
The candidate's projects showcase a strong interest and practical application in cutting-edge AI/ML domains, aligning well with an AI Engineer role. The diversity of projects, from conversational AI chatbots to demand forecasting MLOps pipelines, indicates a broad technical curiosity and willingness to tackle different challenges. Their experience with open-source tools and frameworks suggests a collaborative mindset. The focus on production-readiness in their projects also aligns with industry best practices.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project work, particularly in designing end-to-end MLOps pipelines and RAG systems. Their ability to integrate various tools and frameworks (e.g., Docker, GitHub Actions, MLflow) suggests good operational awareness. The detailed project descriptions indicate a structured approach to development. However, without direct interaction or psychometric test results, assessing stress handling, teamwork, and communication clarity in a collaborative setting is not possible.